Article Dans Une Revue Machine Learning Année : 2025

PASCO (PArallel Structured COarsening): an overlay to speed up graph clustering algorithms

Résumé

Clustering the nodes of a graph is a cornerstone of graph analysis and has been extensively studied. However, some popular methods are not suitable for very large graphs: e.g., spectral clustering requires the computation of the spectral decomposition of the Laplacian matrix, which is not applicable for large graphs with a large number of communities. This work introduces PASCO, an overlay that accelerates clustering algorithms. Our method consists of three steps: 1-We compute several independent small graphs representing the input graph by applying an efficient and structure-preserving coarsening algorithm. 2-A clustering algorithm is run in parallel onto each small graph and provides several partitions of the initial graph. 3-These partitions are aligned and combined with an optimal transport method to output the final partition. The PASCO framework is based on two key contributions: a novel global algorithm structure designed to enable parallelization and a fast, empirically validated graph coarsening algorithm that preserves structural properties. We demonstrate the strong performance of 1 PASCO in terms of computational efficiency, structural preservation, and output partition quality, evaluated on both synthetic and real-world graph datasets.

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Dates et versions

hal-04837207 , version 1 (17-12-2024)
hal-04837207 , version 2 (11-06-2025)

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Etienne Lasalle, Rémi Vaudaine, Titouan Vayer, Pierre Borgnat, Rémi Gribonval, et al.. PASCO (PArallel Structured COarsening): an overlay to speed up graph clustering algorithms. Machine Learning, 2025, 114, pp.212. ⟨10.1007/s10994-025-06837-7⟩. ⟨hal-04837207v2⟩
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